In [1]:
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline
#import os
import tensorflow as tf
In [2]:
print(plt.get_backend())
module://ipykernel.pylab.backend_inline
In [3]:
homeb = pd.read_csv("../nilm_metadata/Smart_microgrid/House wise data/HomeB/2016/HomeB-meter1_2016.csv", index_col=0)
In [4]:
homeb_shape=homeb.shape
homeb_type=type(homeb)
print ("shape : "+str(homeb_shape) +" and type : " + str(homeb_type))
print (homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","Washer [kW]"].describe())
shape : (247600, 17) and type : <class 'pandas.core.frame.DataFrame'>
count    48.000000
mean      0.008688
std       0.021093
min       0.003182
25%       0.003241
50%       0.003357
75%       0.003832
max       0.127577
Name: Washer [kW], dtype: float64
In [5]:
homeb.info
Out[5]:
<bound method DataFrame.info of                      use [kW]  gen [kW]  Grid [kW]   AC [kW]  Furnace [kW]  \
Date & Time                                                                  
2016-01-01 00:00:00  0.455814       0.0   0.455814  0.001166      0.189853   
2016-01-01 00:30:00  0.403376       0.0   0.403376  0.000659      0.104036   
2016-01-01 01:00:00  0.662962       0.0   0.662962  0.000823      0.107150   
2016-01-01 01:30:00  0.677851       0.0   0.677851  0.001426      0.196811   
2016-01-01 02:00:00  0.425556       0.0   0.425556  0.000759      0.119401   
2016-01-01 02:30:00  0.448744       0.0   0.448744  0.000652      0.124341   
2016-01-01 03:00:00  0.751886       0.0   0.751886  0.001643      0.325440   
2016-01-01 03:30:00  0.554862       0.0   0.554862  0.001001      0.195691   
2016-01-01 04:00:00  0.481422       0.0   0.481422  0.000929      0.183667   
2016-01-01 04:30:00  0.640826       0.0   0.640826  0.002342      0.434424   
2016-01-01 05:00:00  0.491982       0.0   0.491982  0.001082      0.211639   
2016-01-01 05:30:00  0.450740       0.0   0.450740  0.000891      0.173173   
2016-01-01 06:00:00  0.390099       0.0   0.390099  0.001082      0.202955   
2016-01-01 06:30:00  0.477034       0.0   0.477034  0.001015      0.196979   
2016-01-01 07:00:00  0.464292       0.0   0.464292  0.001019      0.195085   
2016-01-01 07:30:00  0.421597       0.0   0.421597  0.001191      0.210581   
2016-01-01 08:00:00  0.520880       0.0   0.520880  0.001125      0.206050   
2016-01-01 08:30:00  0.280367       0.0   0.280367  0.000067      0.010183   
2016-01-01 09:00:00  0.280385       0.0   0.280385  0.000074      0.009666   
2016-01-01 09:30:00  0.397311       0.0   0.397311  0.000066      0.009701   
2016-01-01 10:00:00  0.453370       0.0   0.453370  0.000155      0.009593   
2016-01-01 10:30:00  0.432927       0.0   0.432927  0.000079      0.009718   
2016-01-01 11:00:00  0.332945       0.0   0.332945  0.000036      0.009689   
2016-01-01 11:30:00  0.197093       0.0   0.197093  0.000058      0.009607   
2016-01-01 12:00:00  0.212063       0.0   0.212063  0.000057      0.009541   
2016-01-01 12:30:00  0.305679       0.0   0.305679  0.000038      0.009622   
2016-01-01 13:00:00  0.193844       0.0   0.193844  0.000061      0.009536   
2016-01-01 13:30:00  0.201886       0.0   0.201886  0.000058      0.009632   
2016-01-01 14:00:00  0.579818       0.0   0.579818  0.000842      0.009392   
2016-01-01 14:30:00  1.846798       0.0   1.846798  0.007136      0.202987   
...                       ...       ...        ...       ...           ...   
2016-12-31 23:30:00  0.280300       0.0   0.280300  0.000300      0.008817   
2016-12-31 23:31:00  0.279600       0.0   0.279600  0.000283      0.008800   
2016-12-31 23:32:00  0.279767       0.0   0.279767  0.000283      0.008817   
2016-12-31 23:33:00  0.280400       0.0   0.280400  0.000300      0.008817   
2016-12-31 23:34:00  0.280267       0.0   0.280267  0.000283      0.008817   
2016-12-31 23:35:00  0.294933       0.0   0.294933  0.000367      0.023333   
2016-12-31 23:36:00  0.280567       0.0   0.280567  0.000300      0.008817   
2016-12-31 23:37:00  0.280233       0.0   0.280233  0.000300      0.008817   
2016-12-31 23:38:00  0.280700       0.0   0.280700  0.000283      0.008817   
2016-12-31 23:39:00  0.280400       0.0   0.280400  0.000283      0.008833   
2016-12-31 23:40:00  0.280400       0.0   0.280400  0.000300      0.008800   
2016-12-31 23:41:00  0.279817       0.0   0.279817  0.000300      0.008817   
2016-12-31 23:42:00  0.280150       0.0   0.280150  0.000283      0.008833   
2016-12-31 23:43:00  0.280800       0.0   0.280800  0.000300      0.008867   
2016-12-31 23:44:00  0.280633       0.0   0.280633  0.000300      0.008833   
2016-12-31 23:45:00  0.281833       0.0   0.281833  0.000283      0.008900   
2016-12-31 23:46:00  0.281267       0.0   0.281267  0.000300      0.008917   
2016-12-31 23:47:00  0.280867       0.0   0.280867  0.000300      0.008900   
2016-12-31 23:48:00  0.281117       0.0   0.281117  0.000283      0.008900   
2016-12-31 23:49:00  0.329833       0.0   0.329833  0.000283      0.008917   
2016-12-31 23:50:00  0.424533       0.0   0.424533  0.000267      0.008900   
2016-12-31 23:51:00  0.409067       0.0   0.409067  0.000267      0.008933   
2016-12-31 23:52:00  0.408533       0.0   0.408533  0.000267      0.008917   
2016-12-31 23:53:00  0.407117       0.0   0.407117  0.000267      0.008917   
2016-12-31 23:54:00  0.407150       0.0   0.407150  0.000267      0.008933   
2016-12-31 23:55:00  0.406400       0.0   0.406400  0.000267      0.008950   
2016-12-31 23:56:00  0.405350       0.0   0.405350  0.000267      0.008950   
2016-12-31 23:57:00  0.404333       0.0   0.404333  0.000267      0.008900   
2016-12-31 23:58:00  0.403883       0.0   0.403883  0.000267      0.008900   
2016-12-31 23:59:00  0.382550       0.0   0.382550  0.000200      0.008883   

                     Cellar Lights [kW]  Washer [kW]  First Floor lights [kW]  \
Date & Time                                                                     
2016-01-01 00:00:00            0.006457     0.003416                 0.032004   
2016-01-01 00:30:00            0.006415     0.003429                 0.031977   
2016-01-01 01:00:00            0.006254     0.003527                 0.119877   
2016-01-01 01:30:00            0.006257     0.003562                 0.132939   
2016-01-01 02:00:00            0.006401     0.003441                 0.054034   
2016-01-01 02:30:00            0.006592     0.003307                 0.003767   
2016-01-01 03:00:00            0.006718     0.003231                 0.003629   
2016-01-01 03:30:00            0.006653     0.003261                 0.003701   
2016-01-01 04:00:00            0.006651     0.003241                 0.003699   
2016-01-01 04:30:00            0.021909     0.003182                 0.003640   
2016-01-01 05:00:00            0.006612     0.003232                 0.003672   
2016-01-01 05:30:00            0.006599     0.003240                 0.003694   
2016-01-01 06:00:00            0.006612     0.003255                 0.003724   
2016-01-01 06:30:00            0.006719     0.003241                 0.003690   
2016-01-01 07:00:00            0.006712     0.003234                 0.013461   
2016-01-01 07:30:00            0.006651     0.003297                 0.031757   
2016-01-01 08:00:00            0.006836     0.003274                 0.031877   
2016-01-01 08:30:00            0.006673     0.003328                 0.031785   
2016-01-01 09:00:00            0.006619     0.003317                 0.031693   
2016-01-01 09:30:00            0.015209     0.003297                 0.031712   
2016-01-01 10:00:00            0.059444     0.003314                 0.031705   
2016-01-01 10:30:00            0.030112     0.003232                 0.022193   
2016-01-01 11:00:00            0.006735     0.003198                 0.003744   
2016-01-01 11:30:00            0.006667     0.003224                 0.003782   
2016-01-01 12:00:00            0.006624     0.003207                 0.003752   
2016-01-01 12:30:00            0.006697     0.003197                 0.003714   
2016-01-01 13:00:00            0.006616     0.003229                 0.003737   
2016-01-01 13:30:00            0.006681     0.003225                 0.003762   
2016-01-01 14:00:00            0.006339     0.003814                 0.003987   
2016-01-01 14:30:00            0.004072     0.007419                 0.005669   
...                                 ...          ...                      ...   
2016-12-31 23:30:00            0.005800     0.003450                 0.051433   
2016-12-31 23:31:00            0.005783     0.003467                 0.051450   
2016-12-31 23:32:00            0.005817     0.003467                 0.051483   
2016-12-31 23:33:00            0.005800     0.003450                 0.051450   
2016-12-31 23:34:00            0.005817     0.003467                 0.051467   
2016-12-31 23:35:00            0.005817     0.003450                 0.051517   
2016-12-31 23:36:00            0.005817     0.003467                 0.051533   
2016-12-31 23:37:00            0.005817     0.003450                 0.051500   
2016-12-31 23:38:00            0.005833     0.003450                 0.051517   
2016-12-31 23:39:00            0.005817     0.003450                 0.051533   
2016-12-31 23:40:00            0.005817     0.003450                 0.051450   
2016-12-31 23:41:00            0.005800     0.003450                 0.051483   
2016-12-31 23:42:00            0.005817     0.003467                 0.051533   
2016-12-31 23:43:00            0.005833     0.003450                 0.051550   
2016-12-31 23:44:00            0.005817     0.003467                 0.051500   
2016-12-31 23:45:00            0.005817     0.003467                 0.051500   
2016-12-31 23:46:00            0.005817     0.003467                 0.051533   
2016-12-31 23:47:00            0.005833     0.003450                 0.051517   
2016-12-31 23:48:00            0.005817     0.003467                 0.051533   
2016-12-31 23:49:00            0.005850     0.003450                 0.051533   
2016-12-31 23:50:00            0.005883     0.003433                 0.051483   
2016-12-31 23:51:00            0.005867     0.003433                 0.051500   
2016-12-31 23:52:00            0.005867     0.003433                 0.051483   
2016-12-31 23:53:00            0.005883     0.003433                 0.051450   
2016-12-31 23:54:00            0.005883     0.003433                 0.051517   
2016-12-31 23:55:00            0.005900     0.003450                 0.051567   
2016-12-31 23:56:00            0.005883     0.003433                 0.051533   
2016-12-31 23:57:00            0.005900     0.003433                 0.051517   
2016-12-31 23:58:00            0.005900     0.003450                 0.051517   
2016-12-31 23:59:00            0.005917     0.003367                 0.051400   

                     Utility Rm + Basement Bath [kW]  Garage outlets [kW]  \
Date & Time                                                                 
2016-01-01 00:00:00                         0.002309             0.004976   
2016-01-01 00:30:00                         0.002378             0.004958   
2016-01-01 01:00:00                         0.002413             0.012929   
2016-01-01 01:30:00                         0.002367             0.004988   
2016-01-01 02:00:00                         0.002423             0.005025   
2016-01-01 02:30:00                         0.002330             0.004991   
2016-01-01 03:00:00                         0.002167             0.005024   
2016-01-01 03:30:00                         0.002261             0.005003   
2016-01-01 04:00:00                         0.002260             0.004975   
2016-01-01 04:30:00                         0.002108             0.005061   
2016-01-01 05:00:00                         0.002238             0.004973   
2016-01-01 05:30:00                         0.002237             0.004929   
2016-01-01 06:00:00                         0.002234             0.004961   
2016-01-01 06:30:00                         0.002256             0.005004   
2016-01-01 07:00:00                         0.002256             0.004957   
2016-01-01 07:30:00                         0.002279             0.004996   
2016-01-01 08:00:00                         0.002298             0.005044   
2016-01-01 08:30:00                         0.002438             0.004976   
2016-01-01 09:00:00                         0.002424             0.004939   
2016-01-01 09:30:00                         0.002433             0.004939   
2016-01-01 10:00:00                         0.002535             0.004947   
2016-01-01 10:30:00                         0.002444             0.018252   
2016-01-01 11:00:00                         0.002355             0.004806   
2016-01-01 11:30:00                         0.002377             0.004812   
2016-01-01 12:00:00                         0.002354             0.004758   
2016-01-01 12:30:00                         0.002362             0.004798   
2016-01-01 13:00:00                         0.002402             0.004859   
2016-01-01 13:30:00                         0.002399             0.004854   
2016-01-01 14:00:00                         0.002533             0.012481   
2016-01-01 14:30:00                         0.003334             0.005259   
...                                              ...                  ...   
2016-12-31 23:30:00                         0.003333             0.004883   
2016-12-31 23:31:00                         0.003333             0.004917   
2016-12-31 23:32:00                         0.003350             0.004933   
2016-12-31 23:33:00                         0.003333             0.004917   
2016-12-31 23:34:00                         0.003350             0.004917   
2016-12-31 23:35:00                         0.003333             0.004900   
2016-12-31 23:36:00                         0.003350             0.004917   
2016-12-31 23:37:00                         0.003333             0.004917   
2016-12-31 23:38:00                         0.003333             0.004883   
2016-12-31 23:39:00                         0.003333             0.004917   
2016-12-31 23:40:00                         0.003333             0.004900   
2016-12-31 23:41:00                         0.003333             0.004883   
2016-12-31 23:42:00                         0.003317             0.004900   
2016-12-31 23:43:00                         0.003333             0.004900   
2016-12-31 23:44:00                         0.003333             0.004900   
2016-12-31 23:45:00                         0.003333             0.004900   
2016-12-31 23:46:00                         0.003350             0.004917   
2016-12-31 23:47:00                         0.003333             0.004933   
2016-12-31 23:48:00                         0.003350             0.004933   
2016-12-31 23:49:00                         0.003350             0.004950   
2016-12-31 23:50:00                         0.003350             0.004933   
2016-12-31 23:51:00                         0.003333             0.004967   
2016-12-31 23:52:00                         0.003350             0.004967   
2016-12-31 23:53:00                         0.003333             0.004933   
2016-12-31 23:54:00                         0.003333             0.004950   
2016-12-31 23:55:00                         0.003333             0.004950   
2016-12-31 23:56:00                         0.003350             0.004967   
2016-12-31 23:57:00                         0.003350             0.004967   
2016-12-31 23:58:00                         0.003350             0.004967   
2016-12-31 23:59:00                         0.003333             0.004967   

                     MBed + KBed outlets [kW]  Dryer + egauge [kW]  \
Date & Time                                                          
2016-01-01 00:00:00                  0.090664             0.000021   
2016-01-01 00:30:00                  0.090591             0.000068   
2016-01-01 01:00:00                  0.090467             0.000173   
2016-01-01 01:30:00                  0.090688             0.000123   
2016-01-01 02:00:00                  0.077748             0.000040   
2016-01-01 02:30:00                  0.068908             0.000068   
2016-01-01 03:00:00                  0.068744             0.000064   
2016-01-01 03:30:00                  0.063393             0.000081   
2016-01-01 04:00:00                  0.056010             0.000059   
2016-01-01 04:30:00                  0.055681             0.000080   
2016-01-01 05:00:00                  0.055700             0.000038   
2016-01-01 05:30:00                  0.055679             0.000054   
2016-01-01 06:00:00                  0.055396             0.000012   
2016-01-01 06:30:00                  0.055600             0.000050   
2016-01-01 07:00:00                  0.055504             0.000039   
2016-01-01 07:30:00                  0.055432             0.000007   
2016-01-01 08:00:00                  0.055806             0.000056   
2016-01-01 08:30:00                  0.055760             0.000089   
2016-01-01 09:00:00                  0.055729             0.000062   
2016-01-01 09:30:00                  0.055844             0.000131   
2016-01-01 10:00:00                  0.063675             0.000129   
2016-01-01 10:30:00                  0.059654             0.000113   
2016-01-01 11:00:00                  0.055676             0.000128   
2016-01-01 11:30:00                  0.055546             0.000052   
2016-01-01 12:00:00                  0.055494             0.000064   
2016-01-01 12:30:00                  0.055548             0.000127   
2016-01-01 13:00:00                  0.055446             0.000048   
2016-01-01 13:30:00                  0.055501             0.000056   
2016-01-01 14:00:00                  0.278134             0.000270   
2016-01-01 14:30:00                  1.526282             0.000881   
...                                       ...                  ...   
2016-12-31 23:30:00                  0.089400             0.000100   
2016-12-31 23:31:00                  0.089467             0.000067   
2016-12-31 23:32:00                  0.089350             0.000133   
2016-12-31 23:33:00                  0.090067             0.000167   
2016-12-31 23:34:00                  0.090000             0.000067   
2016-12-31 23:35:00                  0.090050             0.000100   
2016-12-31 23:36:00                  0.090067             0.000100   
2016-12-31 23:37:00                  0.090067             0.000133   
2016-12-31 23:38:00                  0.090333             0.000133   
2016-12-31 23:39:00                  0.090067             0.000100   
2016-12-31 23:40:00                  0.090083             0.000100   
2016-12-31 23:41:00                  0.090017             0.000133   
2016-12-31 23:42:00                  0.090167             0.000067   
2016-12-31 23:43:00                  0.090583             0.000133   
2016-12-31 23:44:00                  0.090600             0.000100   
2016-12-31 23:45:00                  0.090917             0.000100   
2016-12-31 23:46:00                  0.090933             0.000100   
2016-12-31 23:47:00                  0.090733             0.000100   
2016-12-31 23:48:00                  0.090667             0.000067   
2016-12-31 23:49:00                  0.090617             0.000133   
2016-12-31 23:50:00                  0.090683             0.000200   
2016-12-31 23:51:00                  0.090617             0.000200   
2016-12-31 23:52:00                  0.090783             0.000200   
2016-12-31 23:53:00                  0.090750             0.000233   
2016-12-31 23:54:00                  0.090700             0.000233   
2016-12-31 23:55:00                  0.090567             0.000200   
2016-12-31 23:56:00                  0.090017             0.000200   
2016-12-31 23:57:00                  0.089817             0.000200   
2016-12-31 23:58:00                  0.089850             0.000200   
2016-12-31 23:59:00                  0.068817             0.000167   

                     Panel GFI (central vac) [kW]  Home Office (R) [kW]  \
Date & Time                                                               
2016-01-01 00:00:00                      0.000347              0.039824   
2016-01-01 00:30:00                      0.000353              0.039098   
2016-01-01 01:00:00                      0.000429              0.038571   
2016-01-01 01:30:00                      0.000429              0.039092   
2016-01-01 02:00:00                      0.000340              0.039470   
2016-01-01 02:30:00                      0.000353              0.039535   
2016-01-01 03:00:00                      0.000434              0.039386   
2016-01-01 03:30:00                      0.000387              0.039337   
2016-01-01 04:00:00                      0.000363              0.039155   
2016-01-01 04:30:00                      0.000349              0.039653   
2016-01-01 05:00:00                      0.000356              0.038921   
2016-01-01 05:30:00                      0.000341              0.038644   
2016-01-01 06:00:00                      0.000299              0.038913   
2016-01-01 06:30:00                      0.000343              0.039547   
2016-01-01 07:00:00                      0.000334              0.039463   
2016-01-01 07:30:00                      0.000306              0.039394   
2016-01-01 08:00:00                      0.000361              0.040014   
2016-01-01 08:30:00                      0.000308              0.039691   
2016-01-01 09:00:00                      0.000287              0.039694   
2016-01-01 09:30:00                      0.000347              0.039563   
2016-01-01 10:00:00                      0.000348              0.039241   
2016-01-01 10:30:00                      0.000334              0.039175   
2016-01-01 11:00:00                      0.000337              0.039387   
2016-01-01 11:30:00                      0.000288              0.039584   
2016-01-01 12:00:00                      0.000296              0.039337   
2016-01-01 12:30:00                      0.000344              0.039386   
2016-01-01 13:00:00                      0.000301              0.039237   
2016-01-01 13:30:00                      0.000301              0.039454   
2016-01-01 14:00:00                      0.000509              0.038450   
2016-01-01 14:30:00                      0.001317              0.039967   
...                                           ...                   ...   
2016-12-31 23:30:00                      0.000367              0.003833   
2016-12-31 23:31:00                      0.000383              0.003850   
2016-12-31 23:32:00                      0.000367              0.003867   
2016-12-31 23:33:00                      0.000367              0.003850   
2016-12-31 23:34:00                      0.000383              0.003867   
2016-12-31 23:35:00                      0.000367              0.003867   
2016-12-31 23:36:00                      0.000383              0.003850   
2016-12-31 23:37:00                      0.000367              0.003867   
2016-12-31 23:38:00                      0.000367              0.003850   
2016-12-31 23:39:00                      0.000383              0.003867   
2016-12-31 23:40:00                      0.000367              0.003867   
2016-12-31 23:41:00                      0.000367              0.003867   
2016-12-31 23:42:00                      0.000383              0.003850   
2016-12-31 23:43:00                      0.000367              0.003867   
2016-12-31 23:44:00                      0.000367              0.003883   
2016-12-31 23:45:00                      0.000367              0.003867   
2016-12-31 23:46:00                      0.000383              0.003867   
2016-12-31 23:47:00                      0.000367              0.003867   
2016-12-31 23:48:00                      0.000367              0.003867   
2016-12-31 23:49:00                      0.000400              0.003817   
2016-12-31 23:50:00                      0.000450              0.003767   
2016-12-31 23:51:00                      0.000450              0.003767   
2016-12-31 23:52:00                      0.000433              0.003783   
2016-12-31 23:53:00                      0.000433              0.003783   
2016-12-31 23:54:00                      0.000450              0.003767   
2016-12-31 23:55:00                      0.000433              0.003800   
2016-12-31 23:56:00                      0.000433              0.003783   
2016-12-31 23:57:00                      0.000450              0.003783   
2016-12-31 23:58:00                      0.000433              0.003800   
2016-12-31 23:59:00                      0.000433              0.003767   

                     Dining room (R) [kW]  Microwave (R) [kW]  Fridge (R) [kW]  
Date & Time                                                                     
2016-01-01 00:00:00              0.000543            0.004767         0.007030  
2016-01-01 00:30:00              0.000783            0.004856         0.043428  
2016-01-01 01:00:00              0.000757            0.066279         0.134296  
2016-01-01 01:30:00              0.000589            0.004738         0.091560  
2016-01-01 02:00:00              0.000666            0.004804         0.004786  
2016-01-01 02:30:00              0.000738            0.004861         0.092701  
2016-01-01 03:00:00              0.000295            0.004696         0.209441  
2016-01-01 03:30:00              0.000607            0.004814         0.143477  
2016-01-01 04:00:00              0.000627            0.004823         0.117937  
2016-01-01 04:30:00              0.000071            0.004592         0.004542  
2016-01-01 05:00:00              0.000574            0.004769         0.101941  
2016-01-01 05:30:00              0.000769            0.004864         0.101054  
2016-01-01 06:00:00              0.000706            0.004843         0.004496  
2016-01-01 06:30:00              0.000714            0.004927         0.103627  
2016-01-01 07:00:00              0.000775            0.004897         0.084492  
2016-01-01 07:30:00              0.000630            0.004870         0.004572  
2016-01-01 08:00:00              0.000728            0.004962         0.114857  
2016-01-01 08:30:00              0.001001            0.005088         0.065381  
2016-01-01 09:00:00              0.001002            0.005067         0.004549  
2016-01-01 09:30:00              0.001049            0.005122         0.117674  
2016-01-01 10:00:00              0.001124            0.005151         0.046622  
2016-01-01 10:30:00              0.001090            0.005120         0.046247  
2016-01-01 11:00:00              0.001072            0.005053         0.119261  
2016-01-01 11:30:00              0.001000            0.004980         0.004462  
2016-01-01 12:00:00              0.001001            0.004949         0.023140  
2016-01-01 12:30:00              0.001001            0.005013         0.125196  
2016-01-01 13:00:00              0.000999            0.004976         0.004481  
2016-01-01 13:30:00              0.000984            0.004975         0.012528  
2016-01-01 14:00:00              0.000749            0.004768         0.131491  
2016-01-01 14:30:00              0.000098            0.004186         0.006492  
...                                   ...                 ...              ...  
2016-12-31 23:30:00              0.046783            0.004850         0.003083  
2016-12-31 23:31:00              0.046000            0.004833         0.003083  
2016-12-31 23:32:00              0.046033            0.004817         0.003083  
2016-12-31 23:33:00              0.046000            0.004833         0.003100  
2016-12-31 23:34:00              0.045983            0.004833         0.003083  
2016-12-31 23:35:00              0.045983            0.004817         0.003100  
2016-12-31 23:36:00              0.046000            0.004817         0.003083  
2016-12-31 23:37:00              0.045917            0.004833         0.003100  
2016-12-31 23:38:00              0.046000            0.004833         0.003083  
2016-12-31 23:39:00              0.045933            0.004817         0.003100  
2016-12-31 23:40:00              0.045900            0.004833         0.003083  
2016-12-31 23:41:00              0.045617            0.004817         0.003083  
2016-12-31 23:42:00              0.045617            0.004833         0.003083  
2016-12-31 23:43:00              0.045633            0.004833         0.003100  
2016-12-31 23:44:00              0.045667            0.004833         0.003100  
2016-12-31 23:45:00              0.045983            0.004833         0.003100  
2016-12-31 23:46:00              0.045733            0.004850         0.003083  
2016-12-31 23:47:00              0.045700            0.004850         0.003100  
2016-12-31 23:48:00              0.045850            0.004850         0.003117  
2016-12-31 23:49:00              0.045850            0.004850         0.051700  
2016-12-31 23:50:00              0.046000            0.004933         0.145600  
2016-12-31 23:51:00              0.045883            0.004917         0.130200  
2016-12-31 23:52:00              0.045983            0.004917         0.129567  
2016-12-31 23:53:00              0.045900            0.004917         0.128517  
2016-12-31 23:54:00              0.045883            0.004917         0.128583  
2016-12-31 23:55:00              0.045867            0.004900         0.127633  
2016-12-31 23:56:00              0.045967            0.004900         0.127083  
2016-12-31 23:57:00              0.045933            0.004917         0.126417  
2016-12-31 23:58:00              0.046017            0.004900         0.125833  
2016-12-31 23:59:00              0.046067            0.004917         0.125283  

[247600 rows x 17 columns]>
In [6]:
print(homeb.head(10))
print(homeb.tail())
                     use [kW]  gen [kW]  Grid [kW]   AC [kW]  Furnace [kW]  \
Date & Time                                                                  
2016-01-01 00:00:00  0.455814       0.0   0.455814  0.001166      0.189853   
2016-01-01 00:30:00  0.403376       0.0   0.403376  0.000659      0.104036   
2016-01-01 01:00:00  0.662962       0.0   0.662962  0.000823      0.107150   
2016-01-01 01:30:00  0.677851       0.0   0.677851  0.001426      0.196811   
2016-01-01 02:00:00  0.425556       0.0   0.425556  0.000759      0.119401   
2016-01-01 02:30:00  0.448744       0.0   0.448744  0.000652      0.124341   
2016-01-01 03:00:00  0.751886       0.0   0.751886  0.001643      0.325440   
2016-01-01 03:30:00  0.554862       0.0   0.554862  0.001001      0.195691   
2016-01-01 04:00:00  0.481422       0.0   0.481422  0.000929      0.183667   
2016-01-01 04:30:00  0.640826       0.0   0.640826  0.002342      0.434424   

                     Cellar Lights [kW]  Washer [kW]  First Floor lights [kW]  \
Date & Time                                                                     
2016-01-01 00:00:00            0.006457     0.003416                 0.032004   
2016-01-01 00:30:00            0.006415     0.003429                 0.031977   
2016-01-01 01:00:00            0.006254     0.003527                 0.119877   
2016-01-01 01:30:00            0.006257     0.003562                 0.132939   
2016-01-01 02:00:00            0.006401     0.003441                 0.054034   
2016-01-01 02:30:00            0.006592     0.003307                 0.003767   
2016-01-01 03:00:00            0.006718     0.003231                 0.003629   
2016-01-01 03:30:00            0.006653     0.003261                 0.003701   
2016-01-01 04:00:00            0.006651     0.003241                 0.003699   
2016-01-01 04:30:00            0.021909     0.003182                 0.003640   

                     Utility Rm + Basement Bath [kW]  Garage outlets [kW]  \
Date & Time                                                                 
2016-01-01 00:00:00                         0.002309             0.004976   
2016-01-01 00:30:00                         0.002378             0.004958   
2016-01-01 01:00:00                         0.002413             0.012929   
2016-01-01 01:30:00                         0.002367             0.004988   
2016-01-01 02:00:00                         0.002423             0.005025   
2016-01-01 02:30:00                         0.002330             0.004991   
2016-01-01 03:00:00                         0.002167             0.005024   
2016-01-01 03:30:00                         0.002261             0.005003   
2016-01-01 04:00:00                         0.002260             0.004975   
2016-01-01 04:30:00                         0.002108             0.005061   

                     MBed + KBed outlets [kW]  Dryer + egauge [kW]  \
Date & Time                                                          
2016-01-01 00:00:00                  0.090664             0.000021   
2016-01-01 00:30:00                  0.090591             0.000068   
2016-01-01 01:00:00                  0.090467             0.000173   
2016-01-01 01:30:00                  0.090688             0.000123   
2016-01-01 02:00:00                  0.077748             0.000040   
2016-01-01 02:30:00                  0.068908             0.000068   
2016-01-01 03:00:00                  0.068744             0.000064   
2016-01-01 03:30:00                  0.063393             0.000081   
2016-01-01 04:00:00                  0.056010             0.000059   
2016-01-01 04:30:00                  0.055681             0.000080   

                     Panel GFI (central vac) [kW]  Home Office (R) [kW]  \
Date & Time                                                               
2016-01-01 00:00:00                      0.000347              0.039824   
2016-01-01 00:30:00                      0.000353              0.039098   
2016-01-01 01:00:00                      0.000429              0.038571   
2016-01-01 01:30:00                      0.000429              0.039092   
2016-01-01 02:00:00                      0.000340              0.039470   
2016-01-01 02:30:00                      0.000353              0.039535   
2016-01-01 03:00:00                      0.000434              0.039386   
2016-01-01 03:30:00                      0.000387              0.039337   
2016-01-01 04:00:00                      0.000363              0.039155   
2016-01-01 04:30:00                      0.000349              0.039653   

                     Dining room (R) [kW]  Microwave (R) [kW]  Fridge (R) [kW]  
Date & Time                                                                     
2016-01-01 00:00:00              0.000543            0.004767         0.007030  
2016-01-01 00:30:00              0.000783            0.004856         0.043428  
2016-01-01 01:00:00              0.000757            0.066279         0.134296  
2016-01-01 01:30:00              0.000589            0.004738         0.091560  
2016-01-01 02:00:00              0.000666            0.004804         0.004786  
2016-01-01 02:30:00              0.000738            0.004861         0.092701  
2016-01-01 03:00:00              0.000295            0.004696         0.209441  
2016-01-01 03:30:00              0.000607            0.004814         0.143477  
2016-01-01 04:00:00              0.000627            0.004823         0.117937  
2016-01-01 04:30:00              0.000071            0.004592         0.004542  
                     use [kW]  gen [kW]  Grid [kW]   AC [kW]  Furnace [kW]  \
Date & Time                                                                  
2016-12-31 23:55:00  0.406400       0.0   0.406400  0.000267      0.008950   
2016-12-31 23:56:00  0.405350       0.0   0.405350  0.000267      0.008950   
2016-12-31 23:57:00  0.404333       0.0   0.404333  0.000267      0.008900   
2016-12-31 23:58:00  0.403883       0.0   0.403883  0.000267      0.008900   
2016-12-31 23:59:00  0.382550       0.0   0.382550  0.000200      0.008883   

                     Cellar Lights [kW]  Washer [kW]  First Floor lights [kW]  \
Date & Time                                                                     
2016-12-31 23:55:00            0.005900     0.003450                 0.051567   
2016-12-31 23:56:00            0.005883     0.003433                 0.051533   
2016-12-31 23:57:00            0.005900     0.003433                 0.051517   
2016-12-31 23:58:00            0.005900     0.003450                 0.051517   
2016-12-31 23:59:00            0.005917     0.003367                 0.051400   

                     Utility Rm + Basement Bath [kW]  Garage outlets [kW]  \
Date & Time                                                                 
2016-12-31 23:55:00                         0.003333             0.004950   
2016-12-31 23:56:00                         0.003350             0.004967   
2016-12-31 23:57:00                         0.003350             0.004967   
2016-12-31 23:58:00                         0.003350             0.004967   
2016-12-31 23:59:00                         0.003333             0.004967   

                     MBed + KBed outlets [kW]  Dryer + egauge [kW]  \
Date & Time                                                          
2016-12-31 23:55:00                  0.090567             0.000200   
2016-12-31 23:56:00                  0.090017             0.000200   
2016-12-31 23:57:00                  0.089817             0.000200   
2016-12-31 23:58:00                  0.089850             0.000200   
2016-12-31 23:59:00                  0.068817             0.000167   

                     Panel GFI (central vac) [kW]  Home Office (R) [kW]  \
Date & Time                                                               
2016-12-31 23:55:00                      0.000433              0.003800   
2016-12-31 23:56:00                      0.000433              0.003783   
2016-12-31 23:57:00                      0.000450              0.003783   
2016-12-31 23:58:00                      0.000433              0.003800   
2016-12-31 23:59:00                      0.000433              0.003767   

                     Dining room (R) [kW]  Microwave (R) [kW]  Fridge (R) [kW]  
Date & Time                                                                     
2016-12-31 23:55:00              0.045867            0.004900         0.127633  
2016-12-31 23:56:00              0.045967            0.004900         0.127083  
2016-12-31 23:57:00              0.045933            0.004917         0.126417  
2016-12-31 23:58:00              0.046017            0.004900         0.125833  
2016-12-31 23:59:00              0.046067            0.004917         0.125283  
In [7]:
homeb_description=homeb.describe()
print (homeb_description)
            use [kW]  gen [kW]      Grid [kW]        AC [kW]   Furnace [kW]  \
count  247600.000000  247600.0  247600.000000  247600.000000  247600.000000   
mean        1.035553       0.0       1.035553       0.342994       0.116331   
std         1.017468       0.0       1.017468       0.868884       0.181299   
min         0.004017       0.0       0.004017       0.000000       0.000003   
25%         0.381183       0.0       0.381183       0.000100       0.009000   
50%         0.670183       0.0       0.670183       0.000350       0.009350   
75%         1.094150       0.0       1.094150       0.002483       0.260437   
max         9.381150       0.0       9.381150       4.303854       0.586683   

       Cellar Lights [kW]    Washer [kW]  First Floor lights [kW]  \
count       247600.000000  247600.000000            247600.000000   
mean             0.008922       0.004404                 0.031873   
std              0.017014       0.015072                 0.039393   
min              0.000017       0.000000                 0.000033   
25%              0.005517       0.003083                 0.003800   
50%              0.005817       0.003233                 0.030450   
75%              0.005983       0.003350                 0.031983   
max              0.186483       0.465350                 0.878296   

       Utility Rm + Basement Bath [kW]  Garage outlets [kW]  \
count                    247600.000000        247600.000000   
mean                          0.160300             0.005410   
std                           0.250489             0.007141   
min                           0.000267             0.000017   
25%                           0.003267             0.004800   
50%                           0.003533             0.004900   
75%                           0.272192             0.004983   
max                           1.546676             0.697683   

       MBed + KBed outlets [kW]  Dryer + egauge [kW]  \
count             247600.000000        247600.000000   
mean                   0.082901             0.022582   
std                    0.122644             0.290628   
min                    0.000033             0.000000   
25%                    0.056850             0.000033   
50%                    0.062683             0.000067   
75%                    0.070833             0.000100   
max                    2.592283             6.054800   

       Panel GFI (central vac) [kW]  Home Office (R) [kW]  \
count                 247600.000000         247600.000000   
mean                       0.000404              0.123603   
std                        0.007269              0.167647   
min                        0.000000              0.000000   
25%                        0.000217              0.003067   
50%                        0.000300              0.016644   
75%                        0.000367              0.339850   
max                        0.768017              0.500833   

       Dining room (R) [kW]  Microwave (R) [kW]  Fridge (R) [kW]  
count         247600.000000       247600.000000    247600.000000  
mean               0.040586            0.012301         0.072069  
std                0.019152            0.102988         0.076897  
min                0.000000            0.000067         0.000867  
25%                0.032767            0.004433         0.004000  
50%                0.033750            0.004683         0.045383  
75%                0.045183            0.004883         0.133167  
max                0.911288            1.923833         1.132350  

homeb_description=homeb.describe(include=['O'])

In [8]:
print(len(homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","AC [kW]"]))
print(homeb.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59","AC [kW]"].shape)
print(homeb.loc["2016-01-03 00:00:00":"2016-01-03 23:59:59","AC [kW]"].shape)
48
(48,)
(48,)

data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),

'Item2' : pd.DataFrame(np.random.randn(4, 2))}

p = pd.Panel(data)

print (p)

import warnings

warnings.simplefilter(action='ignore', category=FutureWarning) daywise={"2016-01-01":homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59",["AC [kW]","Washer [kW]"]]} ac_data_daywise=pd.Panel(daywise)

ac_data_daywise.describe()

print(ac_data_daywise.shape)

type(ac_data_daywise)

In [9]:
ac_data=homeb.loc[:,"AC [kW]"]
washer_data=homeb.loc[:,"Washer [kW]"]
grid_data=homeb.loc[:,"Grid [kW]"]

ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe() ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe()

ac_data_daywise=homeb.loc["2016-09-03 00:00:00":"2016-09-03 23:59:59","AC [kW]"] ac_data_daywise.describe()

In [10]:
import datetime
start_date=datetime.date(2016,1,1)
end_date=datetime.date(2016,12,31)
inc_1_days=datetime.timedelta(days=1)
#print (start_date)
#print (start_date+inc_1_days)
ac_data_list=[]
while (start_date <= end_date):
    #print (start_date)
    #print(len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]))
    #print(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max())
    ac_data_list.append({"date":str(start_date),"no_of_data":len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]),"Peak_Power":homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max()})
    start_date=start_date+inc_1_days
#print (ac_data)
ac_data_day_wise=pd.DataFrame(ac_data_list)
ac_data_day_wise.describe(include='O')
ac_data_day_wise.describe()
ac_data_day_wise_1440=ac_data_day_wise[ac_data_day_wise["no_of_data"]==1440]
ac_data_day_wise.set_index("date",inplace=True)
ac_data_day_wise_1440.set_index("date",inplace=True)
pd.set_option('display.max_rows', 1000)
print(ac_data_day_wise["no_of_data"].value_counts())
print(ac_data_day_wise_1440.info)
48      199
1440    164
1500      1
342       1
46        1
Name: no_of_data, dtype: int64
<bound method DataFrame.info of             Peak_Power  no_of_data
date                              
2016-07-20    2.541317        1440
2016-07-21    2.739450        1440
2016-07-22    2.868167        1440
2016-07-23    2.805167        1440
2016-07-24    2.760617        1440
2016-07-25    2.816733        1440
2016-07-26    2.761800        1440
2016-07-27    2.808667        1440
2016-07-28    2.773983        1440
2016-07-29    2.731933        1440
2016-07-30    2.664517        1440
2016-07-31    2.473683        1440
2016-08-01    2.520417        1440
2016-08-02    2.503650        1440
2016-08-03    2.646683        1440
2016-08-04    2.660650        1440
2016-08-05    2.730233        1440
2016-08-06    2.723617        1440
2016-08-07    2.690383        1440
2016-08-08    2.676767        1440
2016-08-09    2.722517        1440
2016-08-10    2.634617        1440
2016-08-11    2.809017        1440
2016-08-12    2.847450        1440
2016-08-13    2.828167        1440
2016-08-14    2.822833        1440
2016-08-15    2.723917        1440
2016-08-16    2.676733        1440
2016-08-17    2.624067        1440
2016-08-18    2.742367        1440
2016-08-19    2.702283        1440
2016-08-20    2.695400        1440
2016-08-21    2.652767        1440
2016-08-22    2.496350        1440
2016-08-23    2.635567        1440
2016-08-24    2.692117        1440
2016-08-25    2.697917        1440
2016-08-26    2.771733        1440
2016-08-27    2.731900        1440
2016-08-28    2.725483        1440
2016-08-29    2.706500        1440
2016-08-30    2.707133        1440
2016-08-31    2.603433        1440
2016-09-01    2.559233        1440
2016-09-02    2.572700        1440
2016-09-03    0.003183        1440
2016-09-04    0.003433        1440
2016-09-05    0.003267        1440
2016-09-06    2.485983        1440
2016-09-07    2.455917        1440
2016-09-08    2.762900        1440
2016-09-09    2.762267        1440
2016-09-10    2.688500        1440
2016-09-11    2.611383        1440
2016-09-12    0.003100        1440
2016-09-13    0.003383        1440
2016-09-14    0.003450        1440
2016-09-15    0.002967        1440
2016-09-16    0.001733        1440
2016-09-17    0.000750        1440
2016-09-18    2.594850        1440
2016-09-19    2.479600        1440
2016-09-20    2.616400        1440
2016-09-21    2.595167        1440
2016-09-22    2.693433        1440
2016-09-23    2.579983        1440
2016-09-24    0.002900        1440
2016-09-25    0.002517        1440
2016-09-26    0.000933        1440
2016-09-27    0.003017        1440
2016-09-28    0.003867        1440
2016-09-29    0.003467        1440
2016-09-30    0.003033        1440
2016-10-01    0.003917        1440
2016-10-02    0.002950        1440
2016-10-03    0.003417        1440
2016-10-04    0.003083        1440
2016-10-05    0.003467        1440
2016-10-06    0.003033        1440
2016-10-07    0.003200        1440
2016-10-08    0.002667        1440
2016-10-09    0.003083        1440
2016-10-10    0.003800        1440
2016-10-11    0.010300        1440
2016-10-12    0.006000        1440
2016-10-13    0.002850        1440
2016-10-14    0.003083        1440
2016-10-15    0.006183        1440
2016-10-16    0.000283        1440
2016-10-17    0.002733        1440
2016-10-18    0.002983        1440
2016-10-19    0.005633        1440
2016-10-20    0.002767        1440
2016-10-21    0.003217        1440
2016-10-22    0.002883        1440
2016-10-23    0.002550        1440
2016-10-24    0.003767        1440
2016-10-25    0.006300        1440
2016-10-26    0.002900        1440
2016-10-27    0.002950        1440
2016-10-28    0.002783        1440
2016-10-29    0.006450        1440
2016-10-30    0.006367        1440
2016-10-31    0.002817        1440
2016-11-01    0.008817        1440
2016-11-02    0.002483        1440
2016-11-03    0.002383        1440
2016-11-04    0.005617        1440
2016-11-05    0.005150        1440
2016-11-07    0.003067        1440
2016-11-08    0.003217        1440
2016-11-09    0.003517        1440
2016-11-10    0.002967        1440
2016-11-11    0.003033        1440
2016-11-12    0.003000        1440
2016-11-13    0.003000        1440
2016-11-14    0.003083        1440
2016-11-15    0.003100        1440
2016-11-16    0.002783        1440
2016-11-17    0.003117        1440
2016-11-18    0.006500        1440
2016-11-19    0.009517        1440
2016-11-20    0.008400        1440
2016-11-21    0.005567        1440
2016-11-22    0.009583        1440
2016-11-23    0.003567        1440
2016-11-24    0.003683        1440
2016-11-25    0.003017        1440
2016-11-26    0.006117        1440
2016-11-27    0.002917        1440
2016-11-28    0.003500        1440
2016-11-29    0.002983        1440
2016-11-30    0.002850        1440
2016-12-01    0.002967        1440
2016-12-02    0.003100        1440
2016-12-03    0.003000        1440
2016-12-04    0.003650        1440
2016-12-05    0.003417        1440
2016-12-06    0.004017        1440
2016-12-07    0.003050        1440
2016-12-08    0.003600        1440
2016-12-09    0.003167        1440
2016-12-10    0.003183        1440
2016-12-11    0.003567        1440
2016-12-12    0.003067        1440
2016-12-13    0.003350        1440
2016-12-14    0.003800        1440
2016-12-15    0.009500        1440
2016-12-16    0.003733        1440
2016-12-17    0.009033        1440
2016-12-18    0.003533        1440
2016-12-19    0.003533        1440
2016-12-20    0.009300        1440
2016-12-21    0.009250        1440
2016-12-22    0.010150        1440
2016-12-23    0.009300        1440
2016-12-24    0.003300        1440
2016-12-25    0.003133        1440
2016-12-26    0.003533        1440
2016-12-27    0.003600        1440
2016-12-28    0.003183        1440
2016-12-29    0.009717        1440
2016-12-30    0.003233        1440
2016-12-31    0.003283        1440>
In [11]:
plt.figure()
fig, axes = plt.subplots(nrows=2, ncols=2)
ac_data.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59"].plot.line(ax=axes[0,0])
ac_data.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59"].plot.line(style='g', ax=axes[0,0])
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0,1])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g',ax=axes[1,1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(style='g',ax=axes[1,0])
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x12aa3240>
<Figure size 432x288 with 0 Axes>
In [12]:
plt.figure(1)
fig, axes = plt.subplots(nrows=10, ncols=1)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g', ax=axes[1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=axes[2])
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(style='g',ax=axes[3])
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(style='g',ax=axes[4])
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=axes[5])
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(style='g', ax=axes[6])
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=axes[7])
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(style='g',ax=axes[8])
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(style='g',ax=axes[9])
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x12c50080>
<Figure size 432x288 with 0 Axes>
In [13]:
plt.figure(1)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-20")
ax[1].set_title("Grid Data 2016-07-20")
plt.figure(2)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-21")
ax[1].set_title("Grid Data 2016-07-21")
plt.figure(3)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-22")
ax[1].set_title("Grid Data 2016-07-22")
plt.figure(4)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-23")
ax[1].set_title("Grid Data 2016-07-23")
plt.figure(5)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-24")
ax[1].set_title("Grid Data 2016-07-24")
plt.figure(6)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-25")
ax[1].set_title("Grid Data 2016-07-25")
plt.figure(7)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-26")
ax[1].set_title("Grid Data 2016-07-26")
plt.figure(8)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-27")
ax[1].set_title("Grid Data 2016-07-27")
plt.figure(9)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-28")
ax[1].set_title("Grid Data 2016-07-28")
plt.figure(10)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data")
ax[1].set_title("Grid Data")
Out[13]:
Text(0.5,1,'Grid Data')
<Figure size 432x288 with 0 Axes>
In [14]:
training_iters = 2000
learning_rate = 0.001 
batch_size = 128
#both placeholders are of type float
x1 = tf.placeholder("float", [None, 1,1440,1])
y1 = tf.placeholder("int64", [None, 2])
In [15]:
train_X=ac_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]
rows=train_X.size/1440
print("rows = "+str(rows))
train_X=train_X.append(washer_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])

test_X=washer_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]

test_X=test_X.append(ac_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])

print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))

train_X=train_X.values.reshape(164,1,1440,1)
test_X=test_X.values.reshape(164,1,1440,1)

train_Y=pd.DataFrame([])
test_Y=pd.DataFrame([])

print ("after reshape train_X[0] shape= "+ str(train_X.shape[0]))

for i in range(train_X.shape[0]):
    if i<rows:
        train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0}))
    else:    
        train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1}))
for i in range(train_X.shape[0]):
    if i<rows:
        test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1})) 
    else:
        test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0})) 
train_Y.set_index("Sno",inplace=True) 
train_Y=train_Y.values.reshape(164,2)

test_Y.set_index("Sno",inplace=True) 
test_Y=test_Y.values.reshape(164,2)

print("train_Y data = "+str(train_Y))
print ("train_Y shape= "+str(train_Y.shape))
print ("test_Y shape= "+str(test_Y.shape))

print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))
rows = 109.0
train_X size= 236160
train_X shape= (236160,)
after reshape train_X[0] shape= 164
train_Y data = [[1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [1 0]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
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 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]
 [0 1]]
train_Y shape= (164, 2)
test_Y shape= (164, 2)
train_X size= 236160
train_X shape= (164, 1, 1440, 1)
In [16]:
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
    axes[int(i/2),0].plot(train_X[i,0])
    axes[int(i/2),1].plot(train_X[i+1,0])
    if (train_Y[i,0])==1:
        axes[int(i/2),0].set_title("AC Data")
    else:
        axes[int(i/2),0].set_title("Washer Data")
    if (train_Y[i+1,0])==1:
        axes[int(i/2),1].set_title("AC Data")
    else:
        axes[int(i/2),1].set_title("Washer Data")


plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
In [17]:
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
    axes[int(i/2),0].plot(test_X[i,0])
    axes[int(i/2),1].plot(test_X[i+1,0])
    if (test_Y[i,0])==1:
        axes[int(i/2),0].set_title("AC Data")
    else:
        axes[int(i/2),0].set_title("Washer Data")
    if (test_Y[i+1,0])==1:
        axes[int(i/2),1].set_title("AC Data")
    else:
        axes[int(i/2),1].set_title("Washer Data")


plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
In [18]:
def conv2d(x, W, b, strides=1):
    # Conv2D wrapper, with bias and relu activation
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x) 

def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
In [19]:
with tf.variable_scope("",reuse = tf.AUTO_REUSE):
    weights = {
    'wc1': tf.get_variable('W1', shape=(1,3,1,32), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc2': tf.get_variable('W2', shape=(1,3,32,64), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc3': tf.get_variable('W3', shape=(1,3,64,128), initializer=tf.contrib.layers.xavier_initializer()), 
    'wd1': tf.get_variable('W4', shape=(180*128,128), initializer=tf.contrib.layers.xavier_initializer()), 
    'out': tf.get_variable('W5', shape=(128,2), initializer=tf.contrib.layers.xavier_initializer()), 
    }
    
    biases = {
    'bc1': tf.get_variable('B1', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),
    'bc2': tf.get_variable('B2', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
    'bc3': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
    'bd1': tf.get_variable('B4', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
    'out': tf.get_variable('B5', shape=(2), initializer=tf.contrib.layers.xavier_initializer()),
    }
In [20]:
def conv_net(x, weights, biases):  

    # here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
    conv1 = maxpool2d(conv1, k=2)

    # Convolution Layer
    # here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
    conv2 = maxpool2d(conv2, k=2)

    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
    conv3 = maxpool2d(conv3, k=2)


    # Fully connected layer
    # Reshape conv2 output to fit fully connected layer input
    fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Output, class prediction
    # finally we multiply the fully connected layer with the weights and add a bias term. 
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out
In [21]:
pred = conv_net(x1, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y1))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
WARNING:tensorflow:From <ipython-input-21-0d1f03f2adbb>:3: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See tf.nn.softmax_cross_entropy_with_logits_v2.

In [22]:
#Here you check whether the index of the maximum value of the predicted image is equal to the actual labelled image. and both will be a column vector.
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y1, 1))

#calculate accuracy across all the given images and average them out. 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
In [23]:
# Initializing the variables
init = tf.global_variables_initializer()
print(training_iters)
2000
In [24]:
with tf.Session() as sess:
    sess.run(init) 
    train_loss = []
    test_loss = []
    train_accuracy = []
    test_accuracy = []
    saver = tf.train.Saver()
    summary_writer = tf.summary.FileWriter('./Output', sess.graph)
    for i in range(training_iters):
        for batch in range(len(train_X)//batch_size):
            batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
            batch_y = train_Y[batch*batch_size:min((batch+1)*batch_size,len(train_Y))] 
            '''print (batch_x.shape)
            print (batch_x.dtype)
            print (batch_y.shape)
            print (batch_y.dtype)'''
            batch_x.astype(float)
            '''plt.figure()
            plt.plot(batch_x[1,0])'''
            # Run optimization op (backprop).
                # Calculate batch loss and accuracy
            opt = sess.run(optimizer, feed_dict={x1: batch_x,
                                                              y1: batch_y})
            loss, acc = sess.run([cost, accuracy], feed_dict={x1: batch_x,
                                                              y1: batch_y})
        print("Iter " + str(i) + ", Loss= " + \
                      "{:.6f}".format(loss) + ", Training Accuracy= " + \
                      "{:.5f}".format(acc))
        print("Optimization Finished!")

        # Calculate accuracy for all 10000 mnist test images
        test_acc,valid_loss = sess.run([accuracy,cost], feed_dict={x1: test_X, y1: test_Y})
        train_loss.append(loss)
        test_loss.append(valid_loss)
        train_accuracy.append(acc)
        test_accuracy.append(test_acc)
        print("Testing Accuracy:","{:.5f}".format(test_acc))
        save_path = saver.save(sess, "/tmp/model.ckpt",global_step=i,max_to_keep=500,keep_checkpoint_every_n_hours=0.1,)
        //save_path = saver.save(sess, "/tmp/model"+str(i)+".ckpt",max_to_keep=500,keep_checkpoint_every_n_hours=0.5,)
        print ("data saved in","/tmp/model"+str(i)+".ckpt")
    summary_writer.close()
Iter 0, Loss= 1.400497, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model0.ckpt
Iter 1, Loss= 1.461437, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model1.ckpt
Iter 2, Loss= 1.208775, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model2.ckpt
Iter 3, Loss= 0.941657, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model3.ckpt
Iter 4, Loss= 0.698538, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model4.ckpt
Iter 5, Loss= 0.482386, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model5.ckpt
Iter 6, Loss= 0.352138, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model6.ckpt
Iter 7, Loss= 0.333581, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model7.ckpt
Iter 8, Loss= 0.388094, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model8.ckpt
Iter 9, Loss= 0.421045, Training Accuracy= 0.59375
Optimization Finished!
Testing Accuracy: 0.66463
data saved in /tmp/model9.ckpt
Iter 10, Loss= 0.405410, Training Accuracy= 0.59375
Optimization Finished!
Testing Accuracy: 0.66463
data saved in /tmp/model10.ckpt
Iter 11, Loss= 0.362444, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model11.ckpt
Iter 12, Loss= 0.327008, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model12.ckpt
Iter 13, Loss= 0.328009, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model13.ckpt
Iter 14, Loss= 0.353359, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model14.ckpt
Iter 15, Loss= 0.363980, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model15.ckpt
Iter 16, Loss= 0.351866, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model16.ckpt
Iter 17, Loss= 0.332383, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model17.ckpt
Iter 18, Loss= 0.322081, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model18.ckpt
Iter 19, Loss= 0.326000, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model19.ckpt
Iter 20, Loss= 0.335164, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model20.ckpt
Iter 21, Loss= 0.339735, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model21.ckpt
Iter 22, Loss= 0.336444, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model22.ckpt
Iter 23, Loss= 0.328512, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model23.ckpt
Iter 24, Loss= 0.322519, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model24.ckpt
Iter 25, Loss= 0.322648, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model25.ckpt
Iter 26, Loss= 0.327237, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model26.ckpt
Iter 27, Loss= 0.330693, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model27.ckpt
Iter 28, Loss= 0.329727, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model28.ckpt
Iter 29, Loss= 0.325636, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model29.ckpt
Iter 30, Loss= 0.322174, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model30.ckpt
Iter 31, Loss= 0.321787, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model31.ckpt
Iter 32, Loss= 0.324042, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model32.ckpt
Iter 33, Loss= 0.325073, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model33.ckpt
Iter 34, Loss= 0.323922, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model34.ckpt
Iter 35, Loss= 0.321958, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model35.ckpt
Iter 36, Loss= 0.321601, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model36.ckpt
Iter 37, Loss= 0.323007, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model37.ckpt
Iter 38, Loss= 0.323229, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model38.ckpt
Iter 39, Loss= 0.322024, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model39.ckpt
Iter 40, Loss= 0.321299, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model40.ckpt
Iter 41, Loss= 0.321710, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model41.ckpt
Iter 42, Loss= 0.322303, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model42.ckpt
Iter 43, Loss= 0.322050, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model43.ckpt
Iter 44, Loss= 0.321194, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model44.ckpt
Iter 45, Loss= 0.321051, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model45.ckpt
Iter 46, Loss= 0.321480, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model46.ckpt
Iter 47, Loss= 0.321118, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model47.ckpt
Iter 48, Loss= 0.320579, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model48.ckpt
Iter 49, Loss= 0.320925, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model49.ckpt
Iter 50, Loss= 0.320724, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model50.ckpt
Iter 51, Loss= 0.320502, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model51.ckpt
Iter 52, Loss= 0.320246, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model52.ckpt
Iter 53, Loss= 0.319962, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model53.ckpt
Iter 54, Loss= 0.319671, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model54.ckpt
Iter 55, Loss= 0.319412, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model55.ckpt
Iter 56, Loss= 0.319568, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model56.ckpt
Iter 57, Loss= 0.318910, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model57.ckpt
Iter 58, Loss= 0.318595, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model58.ckpt
Iter 59, Loss= 0.318482, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model59.ckpt
Iter 60, Loss= 0.317816, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model60.ckpt
Iter 61, Loss= 0.317835, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model61.ckpt
Iter 62, Loss= 0.317138, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model62.ckpt
Iter 63, Loss= 0.317009, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model63.ckpt
Iter 64, Loss= 0.316519, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model64.ckpt
Iter 65, Loss= 0.316001, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model65.ckpt
Iter 66, Loss= 0.315809, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model66.ckpt
Iter 67, Loss= 0.315066, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model67.ckpt
Iter 68, Loss= 0.314644, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model68.ckpt
Iter 69, Loss= 0.314040, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model69.ckpt
Iter 70, Loss= 0.313515, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model70.ckpt
Iter 71, Loss= 0.312768, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model71.ckpt
Iter 72, Loss= 0.311917, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model72.ckpt
Iter 73, Loss= 0.311080, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model73.ckpt
Iter 74, Loss= 0.310284, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model74.ckpt
Iter 75, Loss= 0.309469, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model75.ckpt
Iter 76, Loss= 0.309138, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model76.ckpt
Iter 77, Loss= 0.313020, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model77.ckpt
Iter 78, Loss= 0.320088, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34756
data saved in /tmp/model78.ckpt
Iter 79, Loss= 0.313529, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model79.ckpt
Iter 80, Loss= 0.309822, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model80.ckpt
Iter 81, Loss= 0.316256, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model81.ckpt
Iter 82, Loss= 0.304300, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model82.ckpt
Iter 83, Loss= 0.311966, Training Accuracy= 0.85156
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model83.ckpt
Iter 84, Loss= 0.303300, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model84.ckpt
Iter 85, Loss= 0.309828, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model85.ckpt
Iter 86, Loss= 0.303584, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model86.ckpt
Iter 87, Loss= 0.306096, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.33537
data saved in /tmp/model87.ckpt
Iter 88, Loss= 0.302265, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model88.ckpt
Iter 89, Loss= 0.302603, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model89.ckpt
Iter 90, Loss= 0.299949, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model90.ckpt
Iter 91, Loss= 0.298681, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model91.ckpt
Iter 92, Loss= 0.299820, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model92.ckpt
Iter 93, Loss= 0.296098, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model93.ckpt
Iter 94, Loss= 0.296184, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.34146
data saved in /tmp/model94.ckpt
Iter 95, Loss= 0.295835, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model95.ckpt
Iter 96, Loss= 0.292744, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model96.ckpt
Iter 97, Loss= 0.292456, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model97.ckpt
Iter 98, Loss= 0.292152, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model98.ckpt
Iter 99, Loss= 0.289842, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model99.ckpt
Iter 100, Loss= 0.287651, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model100.ckpt
Iter 101, Loss= 0.287374, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model101.ckpt
Iter 102, Loss= 0.288161, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model102.ckpt
Iter 103, Loss= 0.287896, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.39024
data saved in /tmp/model103.ckpt
Iter 104, Loss= 0.297124, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model104.ckpt
Iter 105, Loss= 0.290272, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.39024
data saved in /tmp/model105.ckpt
Iter 106, Loss= 0.282895, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model106.ckpt
Iter 107, Loss= 0.278054, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.36585
data saved in /tmp/model107.ckpt
Iter 108, Loss= 0.281646, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.39024
data saved in /tmp/model108.ckpt
Iter 109, Loss= 0.292711, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35366
data saved in /tmp/model109.ckpt
Iter 110, Loss= 0.283844, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model110.ckpt
Iter 111, Loss= 0.275611, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model111.ckpt
Iter 112, Loss= 0.270925, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.38415
data saved in /tmp/model112.ckpt
Iter 113, Loss= 0.274601, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model113.ckpt
Iter 114, Loss= 0.285026, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model114.ckpt
Iter 115, Loss= 0.291005, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.42683
data saved in /tmp/model115.ckpt
Iter 116, Loss= 0.294092, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model116.ckpt
Iter 117, Loss= 0.268952, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.42683
data saved in /tmp/model117.ckpt
Iter 118, Loss= 0.264752, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model118.ckpt
Iter 119, Loss= 0.279243, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model119.ckpt
Iter 120, Loss= 0.263223, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.42683
data saved in /tmp/model120.ckpt
Iter 121, Loss= 0.258197, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model121.ckpt
Iter 122, Loss= 0.267239, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.38415
data saved in /tmp/model122.ckpt
Iter 123, Loss= 0.274499, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.42683
data saved in /tmp/model123.ckpt
Iter 124, Loss= 0.272902, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.38415
data saved in /tmp/model124.ckpt
Iter 125, Loss= 0.250898, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model125.ckpt
Iter 126, Loss= 0.273416, Training Accuracy= 0.87500
Optimization Finished!
Testing Accuracy: 0.44512
data saved in /tmp/model126.ckpt
Iter 127, Loss= 0.314002, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model127.ckpt
Iter 128, Loss= 0.247083, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model128.ckpt
Iter 129, Loss= 0.352731, Training Accuracy= 0.69531
Optimization Finished!
Testing Accuracy: 0.67683
data saved in /tmp/model129.ckpt
Iter 130, Loss= 0.362071, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model130.ckpt
Iter 131, Loss= 0.364971, Training Accuracy= 0.85938
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model131.ckpt
Iter 132, Loss= 0.266649, Training Accuracy= 0.99219
Optimization Finished!
Testing Accuracy: 0.53659
data saved in /tmp/model132.ckpt
Iter 133, Loss= 0.323608, Training Accuracy= 0.92188
Optimization Finished!
Testing Accuracy: 0.85976
data saved in /tmp/model133.ckpt
Iter 134, Loss= 0.305115, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model134.ckpt
Iter 135, Loss= 0.343722, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model135.ckpt
Iter 136, Loss= 0.241287, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.42073
data saved in /tmp/model136.ckpt
Iter 137, Loss= 0.366382, Training Accuracy= 0.60938
Optimization Finished!
Testing Accuracy: 0.66463
data saved in /tmp/model137.ckpt
Iter 138, Loss= 0.242643, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.41463
data saved in /tmp/model138.ckpt
Iter 139, Loss= 0.314098, Training Accuracy= 0.86719
Optimization Finished!
Testing Accuracy: 0.35976
data saved in /tmp/model139.ckpt
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Iter 1207, Loss= 0.000032, Training Accuracy= 1.00000
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Iter 1208, Loss= 0.000032, Training Accuracy= 1.00000
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Iter 1209, Loss= 0.000032, Training Accuracy= 1.00000
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Iter 1210, Loss= 0.000032, Training Accuracy= 1.00000
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Iter 1214, Loss= 0.000032, Training Accuracy= 1.00000
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Iter 1219, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1221, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1222, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1224, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1229, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1230, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1231, Loss= 0.000031, Training Accuracy= 1.00000
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Iter 1232, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1233, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1234, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1235, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1236, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1237, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1238, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1239, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1240, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1241, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1242, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1243, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1244, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1245, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1246, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1247, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1248, Loss= 0.000030, Training Accuracy= 1.00000
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Iter 1249, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1251, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1252, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1253, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1254, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1257, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1258, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1259, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1260, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1261, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1262, Loss= 0.000029, Training Accuracy= 1.00000
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Iter 1268, Loss= 0.000028, Training Accuracy= 1.00000
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Iter 1269, Loss= 0.000028, Training Accuracy= 1.00000
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Iter 1270, Loss= 0.000028, Training Accuracy= 1.00000
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Iter 1271, Loss= 0.000028, Training Accuracy= 1.00000
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Iter 1281, Loss= 0.000028, Training Accuracy= 1.00000
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Iter 1287, Loss= 0.000027, Training Accuracy= 1.00000
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Iter 1295, Loss= 0.000027, Training Accuracy= 1.00000
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Iter 1297, Loss= 0.000027, Training Accuracy= 1.00000
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Iter 1298, Loss= 0.000027, Training Accuracy= 1.00000
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Iter 1299, Loss= 0.000027, Training Accuracy= 1.00000
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Iter 1955, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1955.ckpt
Iter 1956, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1956.ckpt
Iter 1957, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1957.ckpt
Iter 1958, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1958.ckpt
Iter 1959, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1959.ckpt
Iter 1960, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1960.ckpt
Iter 1961, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1961.ckpt
Iter 1962, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1962.ckpt
Iter 1963, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1963.ckpt
Iter 1964, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1964.ckpt
Iter 1965, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1965.ckpt
Iter 1966, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1966.ckpt
Iter 1967, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1967.ckpt
Iter 1968, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1968.ckpt
Iter 1969, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1969.ckpt
Iter 1970, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1970.ckpt
Iter 1971, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1971.ckpt
Iter 1972, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1972.ckpt
Iter 1973, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1973.ckpt
Iter 1974, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1974.ckpt
Iter 1975, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1975.ckpt
Iter 1976, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1976.ckpt
Iter 1977, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1977.ckpt
Iter 1978, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1978.ckpt
Iter 1979, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1979.ckpt
Iter 1980, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1980.ckpt
Iter 1981, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1981.ckpt
Iter 1982, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1982.ckpt
Iter 1983, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1983.ckpt
Iter 1984, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1984.ckpt
Iter 1985, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1985.ckpt
Iter 1986, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1986.ckpt
Iter 1987, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1987.ckpt
Iter 1988, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1988.ckpt
Iter 1989, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1989.ckpt
Iter 1990, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1990.ckpt
Iter 1991, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1991.ckpt
Iter 1992, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1992.ckpt
Iter 1993, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1993.ckpt
Iter 1994, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1994.ckpt
Iter 1995, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1995.ckpt
Iter 1996, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1996.ckpt
Iter 1997, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1997.ckpt
Iter 1998, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1998.ckpt
Iter 1999, Loss= 0.000010, Training Accuracy= 1.00000
Optimization Finished!
Testing Accuracy: 1.00000
data saved in /tmp/model1999.ckpt
In [45]:
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_loss, 'b', label='Training loss')
plt.plot(range(len(train_loss)), test_loss, 'r', label='Test loss')
plt.title('Training and Test loss')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.legend()
plt.show()
In [46]:
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_accuracy, 'b', label='Training Accuracy')
plt.plot(range(len(train_loss)), test_accuracy, 'r', label='Test Accuracy')
plt.title('Training and Test Accuracy')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Acurracy',fontsize=16)
plt.legend()
plt.show()
In [39]:
saver = tf.train.Saver()
with tf.variable_scope("", reuse = True):
    with tf.Session() as sess:
        saver.restore(sess, "/tmp/model1998.ckpt")
        print("Model restored.")
        #sess.run(init)
        #print (tf.variable_scope.get_variable_scope())
        w=tf.get_variable("W1")
        w=w.read_value().eval()
        #print(w.read_value().eval())
        plt.subplot(421)
        plt.subplots_adjust(bottom=0.1, right=4, top=4)
        curr_img = np.reshape(w, (3,32))
        plt.title ("iter 1 W1")
        plt.imshow(curr_img, cmap='gray')
        
        w1=tf.get_variable("W2")
        w1=w1.read_value().eval()
        plt.subplot(423)
        curr_img = np.reshape(w1, (64,96))
        plt.title ("iter 1 W2")
        plt.imshow(curr_img, cmap='gray')
        
        saver.restore(sess, "/tmp/model1999.ckpt")
        print("Model restored.")
        w1=tf.get_variable("W1")
        w1=w1.read_value().eval()
        plt.subplot(422)
        curr_img = np.reshape(w1, (3,32))
        plt.title ("iter 2 W1")
        plt.imshow(curr_img, cmap='gray')
        
        w1=tf.get_variable("W2")
        w1=w1.read_value().eval()
        plt.subplot(424)
        curr_img = np.reshape(w1, (64,96))
        plt.title ("iter 2 W2")
        plt.imshow(curr_img, cmap='gray')
        cax = plt.axes([0.1, 0.5, 0.75, 0.8])
        plt.colorbar(cax=cax)
        
INFO:tensorflow:Restoring parameters from /tmp/model1998.ckpt
Model restored.
INFO:tensorflow:Restoring parameters from /tmp/model1999.ckpt
Model restored.